A Lot More to Do: The Promise and Peril of Panel Data in Political Science

In 1995, Beck and Katz (B&K) instructed readers of APSR on “What to do (and not to do) with timeseries, cross-section data.” Even though this influential paper largely ignored the extensive literature on panel data methods, the simple B&K prescriptions rapidly became the new orthodoxy for practitioners. Our assessment of the intellectual aftermath of this paper, however, does not inspire confidence in the conclusions reached during the past decade. The 135 papers we review show a widespread failure to diagnose and treat common problems of time-series, cross-section (TSCS) data (such as unit heterogeneity), to consider alternative dynamic specifications, to account for autocorrelation, and to acknowledge the unpleasant fact that reliable small-sample methods of estimating dynamic models with unit heterogeneity (which characterizes most TSCS analysis in political science) do not yet exist. Furthermore, we replicate eight papers in prominent journals and find that simple alternative specifications often lead to drastically different conclusions. We summarize many of the statistical issues relative to TSCS data and show that there is far more to do with TSCS data than B&K led us to believe. * We greatly benefited from the comments of Neil Beck, Richard Butler, Damon Cann, Scott Cooper, Jay Goodliffe, Donald Green, Darren Hawkins, Daniel Nielson, and Michael Thies. Joseph Burton provided excellent research assistance. We also express thanks to the authors who graciously provided data for this study and subjected themselves to our critique: Michael Campenni, Gary Cox, M.V. Hood, Quentin Kidd, David Lanoue, Karl Moene, Irwin Morris, Jeffrey Pickering, Steven Poe, Gary Reich, Frances Rosenbluth, Steven Saideman, Samuel Stanton, Neal Tate, Michael Thies, Michael Wallerstein, and Nikolas Zahariadis. These data were provided to us either directly or through a publicly available web site. In either case, the authors’ cooperation is commendable and appreciated.